Modular Robotic Systems: Characteristics and Applications

Employing Modular Robotic Systems (MRS) in different application domains confronts a large number of challenging problems in design, optimization, and planning, and so identifying characteristics of such problems is an important step toward finding proper solution approaches for them. In this paper, we address this issue and provide a comprehensive study on MRS through a structured survey about MRS characteristics and their applications. A novel framework called MITE is proposed to characterize both the properties and applications of MRS from four perspectives of Module, Information, Task, and Environment, based on more than 120 domain-specific features, supplemented by a mapping scheme for describing the interrelations of the four basic aspects of the Task component, namely, Application (for describing high-level tasks such as navigation and rescue), Behavior (for referring to constitutive behaviors like locomotion and manipulation which bring about Applications), Goal (for characterizing the way Behaviors are accomplished), and Operation (for designating activities specific to modular robots, such as self-reconfiguration and gait control). Also, by providing a methodical review on modular robotics, the paper deals with some analyses on recent trends, research gaps and challenges, as well as open problems in the field of MRS.     

A query-by-example music retrieval system using feature and decision fusion

Multimedia Tools and Applications - An attractive topic of Music Information Retrieval (MIR) is focused on query-by-example (QBE), which receives a user-provided query and aims to find the target...     

Planning Robot Navigation among Movable Obstacles (NAMO) through a Recursive Approach

This paper addresses the NP-complete problem of Navigation Among Movable Obstacles (NAMO) in which a robot is required to find a collision-free path toward a goal through manipulating and transferring some movable objects on its way. The robot’s main goal is to optimize a performance criterion such as runtime, length of transit or transfer paths, number of manipulated obstacles, total number of displacements of all objects, etc. We have designed a recursive algorithm capable of solving various NAMO problems, ranging from linear monotone to nonlinear non-monotone, and with convex or concave polygonal obstacles. Through the adopted approach, the original problem is decomposed into recursively-solved subproblems, in each of which only one movable object is manipulated. In each call of the algorithm, first a Visibility Graph determines a path from the robot’s current configuration to an intermediate goal configuration, and then a tentative final configuration for the last object intercepting the path is calculated using the Penetration Depth concept. It is assumed that the objects can be pulled or pushed, but not rotated, in a continuous space, and under such assumptions the method is complete and locally optimal for convex objects, with a worst-case time complexity of O(n⁴3^m) in which m is the number of movable objects and n is the number of all vertices on them. Several computational experiments showed that compared to the existing methods in the literature, the proposed recursive method achieved equal or smaller number of transferred obstacles or the total number of displacements of all objects in majority of the test problems.     

Sensor-Based Motion Planning of Wheeled Mobile Robots in Unknown Dynamic Environments

This paper presents a new sensor-based online method for generating collision-free paths for differential-drive wheeled mobile robots pursuing a moving target amidst dynamic and static obstacles. At each iteration, the set of all collision-free directions are calculated using velocity vectors of the robot relative to each obstacle, forming the Directive Circle (DC), which is the fundamental concept of our proposed method. Then, the best feasible direction close to the optimal direction to the target is selected from the DC, which prevents the robot from being trapped in local minima. Local movements of the robot are governed by the exponential stabilizing control scheme that provides a smooth motion at each step, while considering the robot’s kinematic constraints. The robot is able to catch the target at a desired orientation. Extensive simulations demonstrated the efficiency of the proposed method and its success in coping with complex and highly dynamic environments with arbitrary obstacle shapes.     

A Sequential Bi-criteria Search Algorithm for Robot Path Planning in the Box Pushing Problem

The collision-free trajectory planning method subject to control constraints for mobile manipulators is presented. The robot task is to move from the current configuration to a given final position in the workspace. The motions are planned in order to maximise an instantaneous manipulability measure to avoid manipulator singularities. Inequality constraints on state variables i.e. collision avoidance conditions and mechanical constraints are taken into consideration. The collision avoidance is accomplished by local perturbation of the mobile manipulator motion in the obstacles neighbourhood. The fulfilment of mechanical constraints is ensured by using a penalty function approach. The proposed method guarantees satisfying control limitations resulting from capabilities of robot actuators by applying the trajectory scaling approach. Nonholonomic constraints in a Pfaffian form are explicitly incorporated into the control algorithm. A computer example involving a mobile manipulator consisting of nonholonomic platform (2,0) class and 3DOF RPR type holonomic manipulator operating in a three-dimensional task space is also presented.     

A voronoi diagram‐visibility graph‐potential field compound algorithm for robot path planning

Numerous methods have been developed to solve the motion planning problem, among which the Voronoi diagram, visibility graph, and potential fields are well‐known techniques. In this paper, a new path planning algorithm is presented where these three methods are integrated for the first time in a single architecture. After constructing the generalized Voronoi diagram of C‐space, we introduce a novel procedure for its abstraction, producing a pruned generalized Voronoi diagram. A broad freeway net is then developed through a new α‐MID (maximal inscribed discs) concept. A potential function is assigned to the net to form an obstacle‐free network of valleys. Afterwards we take advantage of a bidirectional search, where the visibility graph and potential field modules execute alternately from both start and goal configurations. A steepest descent mildest ascent search technique is used for local planning and avoiding local minima. The algorithm provides a parametric tradeoff between safest and shortest paths and generally yields shorter paths than the Voronoi and potential field methods, and faster than the visibility graph. It also performs well in complicated environments. © 2004 Wiley Periodicals, Inc.